DL : Intro ok

diff --git a/paper.tex b/paper.tex
index 87949b4c9c2be6ac6f16d2037979490b9c801311..4f0fd297961db3ce5c77d8db68d3b5232aa6adc3 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -116,15 +116,15 @@ simulation tool before.
 The use of multi-core architectures for solving large scientific problems seems to  become imperative  in  a  lot  of  cases. 
 Whatever the scale of these architectures (distributed clusters, computational grids, embedded multi-core \ldots) they  are generally 
 well adapted to execute complexe parallel applications operating on a large amount of data.  Unfortunately,  users (industrials or scientists), 
 The use of multi-core architectures for solving large scientific problems seems to  become imperative  in  a  lot  of  cases. 
 Whatever the scale of these architectures (distributed clusters, computational grids, embedded multi-core \ldots) they  are generally 
 well adapted to execute complexe parallel applications operating on a large amount of data.  Unfortunately,  users (industrials or scientists), 

-who need such computational resources may not have an easy access to such efficient architectures. The cost of using the platform and/or the cost of 
+who need such computational resources, may not have an easy access to such efficient architectures. The cost of using the platform and/or the cost of 

 testing and deploying an application are often very important.  So, in this context it is difficult to optimize a given application for a given 
 architecture. In this way and in order to reduce the access cost to these computing resources it seems very interesting to use a simulation environment. 
 testing and deploying an application are often very important.  So, in this context it is difficult to optimize a given application for a given 
 architecture. In this way and in order to reduce the access cost to these computing resources it seems very interesting to use a simulation environment. 

-The advantages are numerous: development life cycle, code debugging, ability to obtain results quickly \ldots
+The advantages are numerous: development life cycle, code debugging, ability to obtain results quickly \ldots at the condition that the simulation results are in education with the real ones.

 
 In this paper we focus on a class of highly efficient parallel algorithms called \emph{iterative algorithms}. The
 parallel scheme of iterative methods is quite simple. It generally involves the division of the problem
 into  several  \emph{blocks}  that  will  be  solved  in  parallel  on  multiple
 
 In this paper we focus on a class of highly efficient parallel algorithms called \emph{iterative algorithms}. The
 parallel scheme of iterative methods is quite simple. It generally involves the division of the problem
 into  several  \emph{blocks}  that  will  be  solved  in  parallel  on  multiple

-processing units.  Then each processing unit has to
+processing units.  Each processing unit has to

 compute an iteration, to send/receive some data dependencies to/from
 its neighbors and to iterate this process until the convergence of
 the method. Several well-known methods demonstrate the convergence of these algorithms~\cite{BT89,Bahi07}.
 compute an iteration, to send/receive some data dependencies to/from
 its neighbors and to iterate this process until the convergence of
 the method. Several well-known methods demonstrate the convergence of these algorithms~\cite{BT89,Bahi07}.


@@ -136,29 +136,33 @@ communication and computations are asynchronous inducing that there is
 no more idle times, due to synchronizations, between two
 iterations~\cite{bcvc06:ij}. This model presents some advantages and drawbacks that we detail in section 2 but even if the number of iterations required to converge is 
 generally  greater  than for the synchronous  case, it appears that the asynchronous  iterative scheme  can significantly  reduce  overall execution
 no more idle times, due to synchronizations, between two
 iterations~\cite{bcvc06:ij}. This model presents some advantages and drawbacks that we detail in section 2 but even if the number of iterations required to converge is 
 generally  greater  than for the synchronous  case, it appears that the asynchronous  iterative scheme  can significantly  reduce  overall execution

-times by  suppressing idle  times due to  synchronizations~\cite{Bahi07} for more details.
+times by  suppressing idle  times due to  synchronizations~(see \cite{Bahi07} for more details).

 
 Nevertheless, in both cases (synchronous or asynchronous) it is very time consuming to find optimal configuration and deployment requirements 
 for a given application on a given multi-core architecture. Finding good resource allocations policies under varying CPU power, network speeds and 
 loads is very challenging and labor intensive.~\cite{Calheiros:2011:CTM:1951445.1951450}.   This problematic is even more difficult for the asynchronous scheme 
 where variations of the parameters of the execution platform can lead to very different number of iterations required to converge and so to very different execution times.
 
 Nevertheless, in both cases (synchronous or asynchronous) it is very time consuming to find optimal configuration and deployment requirements 
 for a given application on a given multi-core architecture. Finding good resource allocations policies under varying CPU power, network speeds and 
 loads is very challenging and labor intensive.~\cite{Calheiros:2011:CTM:1951445.1951450}.   This problematic is even more difficult for the asynchronous scheme 
 where variations of the parameters of the execution platform can lead to very different number of iterations required to converge and so to very different execution times.

-In this challenging context we think that the use of a simulation tool can leverage the possibility of testing various platform scenarios.
+In this challenging context we think that the use of a simulation tool can greatly leverage the possibility of testing various platform scenarios.

 
 

-The main contribution of this paper is to show that the use of a simulation tool (i.e. the SimGrid   toolkit~\cite{SimGrid}) in the context of real 
+The main contribution of this paper is to show that the use of a simulation tool (i.e. the SimGrid toolkit~\cite{SimGrid}) in the context of real 

 parallel applications (i.e. large linear system solver) can help developers to better tune their application for a given multi-core architecture.
 To show the validity of this approach we first compare the simulated execution of the multisplitting algorithm  with  the  GMRES   (Generalized   Minimal  Residual) solver
 parallel applications (i.e. large linear system solver) can help developers to better tune their application for a given multi-core architecture.
 To show the validity of this approach we first compare the simulated execution of the multisplitting algorithm  with  the  GMRES   (Generalized   Minimal  Residual) solver

-\cite{ref1} both in synchronous mode. The obtained results on different simulated multi-core architectures confirm the results previously obtained on non simulated architecture. 
+\cite{ref1} in synchronous mode. The obtained results on different simulated multi-core architectures confirm the real results previously obtained on non simulated architectures. 

 We also confirm  the efficiency  of the  asynchronous  multisplitting algorithm  comparing to the synchronous  GMRES. In this way and with a simple computing architecture (a laptop) 
 We also confirm  the efficiency  of the  asynchronous  multisplitting algorithm  comparing to the synchronous  GMRES. In this way and with a simple computing architecture (a laptop) 

-SimGrid allows us (with small modifications of the MPI code) to run a test campaign  of  a  real parallel iterative  applications on  different simulated multi-core architectures. 
+SimGrid allows us to run a test campaign  of  a  real parallel iterative  applications on  different simulated multi-core architectures. 

 To our knowledge, there is no related work on the large-scale multi-core simulation of a real synchronous and asynchronous iterative application.  
 
 To our knowledge, there is no related work on the large-scale multi-core simulation of a real synchronous and asynchronous iterative application.  
 

-This paper is organized as follows:
+This paper is organized as follows. Section 1 \ref{sec:synchro} presents the iteration model we use and more particularly the asynchronous scheme. 
+In section \ref{sec:simgrid} the SimGrid simulation toolkit is presented. Section \ref{sec:04} details the different solvers that we use. 
+Finally our experimental results are presented in section \ref{\sec:expe} followed by some concluding remarks and perspectives.

 
 
 \section{The asynchronous iteration model}
 
 
 \section{The asynchronous iteration model}

+\label{sec:asynchro}

 
 \section{SimGrid}
 
 \section{SimGrid}

-
+ \label{sec:simgrid}
+ 

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@@ -281,6 +285,7 @@ At last, note that the two solver algorithms have been executed with the Simgrid
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 \section{Experimental Results}
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 \section{Experimental Results}

+\label{sec:expe}

 
 
 \subsection{Setup study and Methodology}
 
 
 \subsection{Setup study and Methodology}